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Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip\u002Fbr), and practical usage for retrieval, clustering, and RAG.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"venice-ai","Venice AI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fvenice-ai.png","veniceai",[13,17],{"name":14,"slug":15,"type":16},"LLM","llm","tag",{"name":18,"slug":19,"type":16},"Data Analysis","data-analysis",119,"https:\u002F\u002Fgithub.com\u002Fveniceai\u002Fskills","2026-07-17T06:07:34.97752",null,15,[],{"repoUrl":21,"stars":20,"forks":24,"topics":27,"description":28},[],"Agent Skills for the Venice.ai API. One folder per surface area, each with a SKILL.md for agent runtimes (Cursor, Claude, Codex, etc.).","https:\u002F\u002Fgithub.com\u002Fveniceai\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fvenice-embeddings","---\nname: venice-embeddings\ndescription: Call POST \u002Fembeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip\u002Fbr), and practical usage for retrieval, clustering, and RAG.\n---\n\n# Venice Embeddings\n\n`POST \u002Fapi\u002Fv1\u002Fembeddings` returns vector embeddings for strings. It's OpenAI-compatible: the request and response match `https:\u002F\u002Fapi.openai.com\u002Fv1\u002Fembeddings` closely enough that the OpenAI SDK works out of the box with `baseURL: \"https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\"`.\n\n## Use when\n\n- You're building retrieval \u002F RAG \u002F similarity search.\n- You need text clustering, classification, deduplication, or reranking.\n- You want Venice's \"no-training, no-retention\" stance on inference inputs — embeddings are generated and returned; the API does not publish E2EE semantics on `\u002Fembeddings` the way it does on selected chat models.\n\nText-only. For image\u002Fmultimodal signals, either run images through a vision chat model and embed the description, or pick a multimodal-capable embedding model from `GET \u002Fmodels?type=embedding` (the catalog changes; inspect `model_spec` on each row).\n\n## Minimal request\n\n```bash\ncurl https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Fembeddings \\\n  -H \"Authorization: Bearer $VENICE_API_KEY\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -H \"Accept-Encoding: gzip, br\" \\\n  -d '{\n    \"model\": \"text-embedding-bge-m3\",\n    \"input\": \"Why is the sky blue?\"\n  }'\n```\n\n```json\n{\n  \"object\": \"list\",\n  \"model\": \"text-embedding-bge-m3\",\n  \"data\": [\n    { \"object\": \"embedding\", \"index\": 0, \"embedding\": [0.0023, -0.0093, 0.0158, ...] }\n  ],\n  \"usage\": { \"prompt_tokens\": 8, \"total_tokens\": 8 }\n}\n```\n\n## Request schema\n\n| Field | Type | Notes |\n|---|---|---|\n| `model` | string | **Required.** Model ID from `GET \u002Fmodels?type=embedding`. |\n| `input` | string \\| string[] \\| number[] \\| number[][] | **Required.** Single string, array of strings (≤ 2048 entries), or pre-tokenized arrays. |\n| `encoding_format` | `\"float\"` \\| `\"base64\"` | Default `\"float\"`. Use `\"base64\"` for ~4× payload shrinkage; decode client-side. |\n| `dimensions` | integer | Optional. Truncate output dimensions. Only meaningful when the model's `model_spec.supportsCustomDimensions === true` — behavior on non-supporting models is model-dependent; test a small call before relying on it. |\n| `user` | string | Accepted for OpenAI compat. Discarded by Venice. |\n\n`input` max tokens per string is capped at the model's `model_spec.maxInputTokens` (typically 8192). Batch arrays are capped at **2048 items**. Venice returns one embedding per element, in order, with matching `index`.\n\n## Response headers & compression\n\nRequest `Accept-Encoding: gzip, br`. The response will include `Content-Encoding` accordingly. For long batches this matters — vectors are large.\n\nFor x402 auth, `X-Balance-Remaining` reports your remaining USDC credits.\n\n## Using the OpenAI SDK\n\n```ts\nimport OpenAI from 'openai'\n\nconst client = new OpenAI({\n  apiKey: process.env.VENICE_API_KEY,\n  baseURL: 'https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1',\n})\n\nconst res = await client.embeddings.create({\n  model: 'text-embedding-bge-m3',\n  input: ['first doc', 'second doc'],\n})\n\nconst vec0 = res.data[0].embedding\n```\n\n## Batch-embedding pattern\n\n```ts\nasync function embedBatch(texts: string[], batchSize = 64) {\n  const out: number[][] = []\n  for (let i = 0; i \u003C texts.length; i += batchSize) {\n    const slice = texts.slice(i, i + batchSize)\n    const res = await client.embeddings.create({\n      model: 'text-embedding-bge-m3',\n      input: slice,\n      encoding_format: 'float',\n    })\n    for (const row of res.data) out[i + row.index] = row.embedding\n  }\n  return out\n}\n```\n\n- Keep batches ≤ model context limit total tokens.\n- On `429`, back off exponentially and halve the batch — see [`venice-errors`](..\u002Fvenice-errors\u002FSKILL.md).\n\n## Choosing a model\n\nQuery `GET \u002Fmodels?type=embedding` for the current catalog. Each entry exposes:\n\n- `model_spec.embeddingDimensions` — native output dimension (e.g. 1024 for BGE-M3).\n- `model_spec.maxInputTokens` — max tokens per input string.\n- `model_spec.supportsCustomDimensions` — whether `dimensions` can truncate the output.\n- `model_spec.pricing.input.usd` \u002F `.diem` — cost per **million** input tokens.\n\nBuilt-in options include `text-embedding-bge-m3`, `text-embedding-bge-en-icl`, `text-embedding-qwen3-8b`, `text-embedding-qwen3-0-6b`, `text-embedding-multilingual-e5-large-instruct`, `text-embedding-3-small`, `text-embedding-3-large`, `gemini-embedding-2-preview`, `text-embedding-nemotron-embed-vl-1b-v2`.\n\nAlways pin the model ID — cosine distances are **not** comparable across different embedding models.\n\n## Error handling\n\n| Code | Meaning |\n|---|---|\n| `400` | Validation error. Check `details` in the response for the exact field. |\n| `401` | Auth \u002F Pro-only model. |\n| `402` | Insufficient balance. Bearer → `INSUFFICIENT_BALANCE`. x402 → structured `PAYMENT_REQUIRED`. |\n| `415` | Wrong `Content-Type` — must be `application\u002Fjson`. |\n| `429` | Rate limited. |\n| `500` | Inference failed; retry with jitter. |\n| `503` | Model at capacity; retry later. |\n\n## Gotchas\n\n- `dimensions` is only meaningful when `model_spec.supportsCustomDimensions === true`. 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